This blog looks at some of the risks associated with data mesh and why organizations need to look at more than just the concepts of distributed data management to ensure successful data mesh. Companies need to evaluate the needs for managing their data products, data governance, the use of data platforms, and how business domains will be managed across the data ecosystem.

Evaluating risks means looking at:

  • data and pipeline complexity

  • data quality

  • security and privacy needs

  • people and resources

  • overall integration needs

Enabling likely success means understanding these risk factors and ensuring that initiatives limit bottlenecks to success. This includes looking at enabling:

  • data governance

  • data standards

  • a data driven culture

  • increased automation

  • data quality and metrics monitoring


Data Mesh is a relatively new concept in the field of data architecture, which proposes a more decentralized approach to data management in organizations. While it has many potential benefits, there are also some risks associated with this approach. Organizations have struggled to gain visibility into their data and gain value from analytics outputs because data management is complex and there’re too many challenges involved in building a strong ecosystem. These complexities sometimes leave business stakeholders feeling like they aren’t gaining the desired value out of their data due to a lack of access. Data mesh eliminates this by managing business domains and ensuring data ownership based on these business domains.

Understanding the risks associated with data mesh

Mitigating risks means understanding them. Here are some of the risks associated with Data Mesh:

  1. Complexity: One of the main risks of implementing Data Mesh is that it can add a layer of complexity to the data architecture. Each domain team may have their own data infrastructure, tools, and processes, which can make it challenging to manage and govern data consistently across the organization. Some organizations lean towards standardization or leveraging apis to increase interoperability among systems. The reality, however, is that domain-oriented approaches require a best fit solution for each area of decentralization which works against a traditional data warehouse or centralized data management approach, adding additional complexities across sources and systems.

  2. Data quality: With data mesh, domain teams are responsible for managing their own data, which can increase the risk of data quality issues. Without proper data governance and oversight, there is a possibility that data may be incomplete, inaccurate, or inconsistent. Issues such as redundancies and duplication are just the obvious areas of concern for a data mesh approach, but there are others. Organizations looking at adding new data sets as part of a business domain may create additional redundancies or data quality challenges as the whole ecosystem isn’t being considered. Adding a self-service approach to the mix means that business intelligence consumption may move from a governed access point to a distributed approach, adding the need for more data governance and data quality parameters.

  3. Integration: Data mesh may lead to the proliferation of data silos, which can make it difficult to integrate data across different domains. This can lead to challenges in creating a unified view of data across the organization. Many organizations are leveraging data lakes which require a variety of use cases for data teams to make sense of the data and create valuable outcomes. Consequently, data integration requirements also become more complex. Data sources may be needed for several business domains so data pipelines and data source access increase in complexity.

  4. Security: With data mesh, sensitive data may be spread across different domain teams, which can increase the risk of data breaches or unauthorized access to sensitive information. Privacy and compliance requirements need to ensure consistency across the organization. Using a data mesh architecture requires security standardization and management by data teams and data engineers, ensuring consistency across business domains.

  5. Talent: Data mesh requires a new set of skills and expertise to manage, which may be challenging for organizations to find and hire. Without the right talent, the benefits of data mesh may not be fully realized. Organizations need to invest in their data teams and create the right domain teams to ensure there is an understanding of how data organized by business domain fits into the larger data ecosystem.

Overall, while data mesh has the potential to bring significant benefits to organizations, it is important to carefully consider the risks and challenges associated with this approach before implementing it. Proper planning, governance, and oversight can help mitigate these risks and ensure that data mesh is implemented successfully. But even beyond implementation, organizations need to make sure that data mesh concepts are understood across the organization and that data governance practices are an integral part of any domain ownership to ensure successful implementation.

Mitigating data mesh risks and evening the odds of data mesh success

Here are some of the best ways to overcome the risks associated with data mesh:

  1. Define clear data governance: Data mesh requires clear governance to ensure that data is managed consistently across the organization. This includes defining data ownership, access rights, and responsibilities for data quality and security. It's important to establish a clear governance framework that aligns with the organization's data strategy and objectives. Additionally, domain ownership and data sharing across domains remains important. Each business domain should be involved in defining and managing the data governance process.

  2. Implement data standards: Consistent data standards ensure that data is accessible and understandable across different teams and systems. Standards should be established for data formats, metadata, and data quality. Many organizations will also look towards their data scientists and data warehousing needs to ensure that data standards are aligned to business intelligence needs as well as their data mesh approach.

  3. Foster a data-driven culture: Data mesh requires a shift in mindset towards data ownership and collaboration. Teams should be encouraged to share data and insights to improve data quality and drive better decision-making. Becoming more data-driven requires a commitment to data literacy and creates dependencies across domain ownership.

  4. Invest in data infrastructure: Data mesh requires a robust data infrastructure that supports data integration, data discovery, and data access. This includes investing in data cataloging tools, data pipelines, and data warehouses. One approach does not mitigate the need for another. Data warehouses, data lakes, data fabrics, and data mesh can co-exist providing all other data management initiatives are aligned and maintained centrally by a data team that understands concepts and the diverse needs across business domains.

  5. Embrace automation: Automation can help reduce the risk of errors and inconsistencies in data management. Tools such as automated testing, data profiling, and data validation can help ensure that data is accurate and reliable.

  6. Monitor and measure: Regular monitoring and measurement of data quality, performance, and usage are critical to identifying and addressing issues quickly. Metrics should be established to track data quality, data availability, and data usage.

By adopting these best practices, organizations can overcome the risks associated with data mesh and are more likely to realize the benefits of this innovative approach to data management.